In this episode of Business Tech Perspectives, I sit down with Rajan Sethuraman, CEO of LatentView Analytics, which is a global data engineering and analytics company that helps businesses excel in the digital world by harnessing the power of data. I learn more about their refreshingly pragmatic approach to AI adoption that many organizations are overlooking.
Rajan brings a rare blend of leadership experience to the table. Before becoming CEO, he spent more than two decades at Accenture, including a role leading talent and people strategy. He later joined LatentView, eventually guiding the company through its IPO and its first acquisition while expanding its work with more than 50 Fortune 500 clients.
Our conversation begins with an idea Rajan describes as AI minimalism. At a time when many executives feel pressure to experiment with every new generative AI capability, Rajan argues that the real challenge is not adopting more technology. Instead, organizations need to simplify their data ecosystems and create trusted foundations before scaling AI initiatives. Without that clarity, companies often end up with multiple data pipelines, conflicting metrics, and competing versions of the truth.
We also talk about the hidden friction inside many AI projects. Rajan explains that technology is rarely the real barrier. Culture and clarity often determine whether a transformation succeeds. If organizations cannot agree on how key metrics are defined or where their source of truth lives, even the most advanced AI models will struggle to deliver meaningful results.
Rajan also shares what he has learned working with global enterprises across industries such as financial services, retail, healthcare, and technology. From governance and data lineage to embedding analytics into everyday decision making, he outlines the patterns that separate organizations that claim to be data driven from those that actually operate that way.
One of the most valuable moments in the conversation comes when Rajan offers practical advice for CEOs under pressure to accelerate AI adoption. His recommendation is surprisingly simple. Start by defining the metrics that matter most to the business. Then work backwards from those metrics to identify the data, systems, and decision processes that influence them. Only after that foundation exists should organizations decide which AI capabilities to deploy.
We also explore how LatentView is helping enterprises apply emerging technologies such as generative and agentic AI to improve efficiency, effectiveness, and speed across business operations. Rajan explains why partnerships, experimentation, and ecosystem collaboration are becoming essential as the AI landscape evolves.
If you are trying to cut through the noise surrounding AI and focus on what actually drives measurable outcomes, this episode offers a thoughtful and practical perspective. Are organizations moving too fast in the race to adopt AI, and could a simpler, more disciplined approach actually create stronger results?